论文标题
FAPM：实时工业异常检测的快速自适应补丁记忆
FAPM: Fast Adaptive Patch Memory for Real-time Industrial Anomaly Detection
论文作者
论文摘要
基于特征嵌入的方法通过将目标图像的特征与正常图像进行比较，在检测工业异常方面表现出了出色的性能。但是，某些方法不符合实时推理的速度要求，这对于现实世界应用至关重要。为了解决这个问题，我们提出了一种新方法，称为“快速自适应补丁记忆（FAPM）”，用于实时工业异常检测。 FAPM利用贴片和图层的内存库，分别将图像的嵌入功能存储在补丁和层级别上，从而消除了不必要的重复计算。我们还建议通过斑块的自适应核心采样，以更快，更准确的检测。与其他最先进的方法相比，FAPM的精度和速度都很好
Feature embedding-based methods have shown exceptional performance in detecting industrial anomalies by comparing features of target images with normal images. However, some methods do not meet the speed requirements of real-time inference, which is crucial for real-world applications. To address this issue, we propose a new method called Fast Adaptive Patch Memory (FAPM) for real-time industrial anomaly detection. FAPM utilizes patch-wise and layer-wise memory banks that store the embedding features of images at the patch and layer level, respectively, which eliminates unnecessary repetitive computations. We also propose patch-wise adaptive coreset sampling for faster and more accurate detection. FAPM performs well in both accuracy and speed compared to other state-of-the-art methods
